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Hamiltonian Monte Carlo

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Model Perspective
Model Perspective
Oct 22, 2022 · Fundamentals

Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work

This article explains the principles behind Markov Chain Monte Carlo methods, including Monte Carlo sampling, the Metropolis‑Hastings algorithm, and the Hamiltonian Monte Carlo (HMC) approach, illustrating how they efficiently approximate posterior distributions in Bayesian analysis.

Bayesian inferenceHamiltonian Monte CarloMCMC
0 likes · 11 min read
Unlocking Bayesian Sampling: How MCMC and Hamiltonian Monte Carlo Work
Model Perspective
Model Perspective
Oct 15, 2022 · Fundamentals

Unlock Faster Bayesian Sampling: How Hamiltonian Monte Carlo Works

Hamiltonian Monte Carlo (HMC) is a rapid sampling technique that improves upon traditional MCMC by leveraging Hamiltonian dynamics, using position and velocity to define potential and kinetic energy, and follows a series of steps—including momentum sampling, leapfrog integration, and Metropolis acceptance—to efficiently explore complex probability distributions.

Bayesian SamplingHamiltonian Monte CarloMCMC
0 likes · 4 min read
Unlock Faster Bayesian Sampling: How Hamiltonian Monte Carlo Works